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<a href="_r_f_trainer_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">//===========================================================================</span><span class="comment"></span></div>
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment">/*!</span></div>
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment"> * </span></div>
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment"> *</span></div>
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment"> * \brief       Random Forest Trainer</span></div>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span><span class="comment"> * </span></div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="comment"> * </span></div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="comment"> *</span></div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="comment"> * \author      K. N. Hansen, J. Kremer</span></div>
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="comment"> * \date        2011-2012</span></div>
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="comment"> *</span></div>
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="comment"> *</span></div>
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span><span class="comment"> * \par Copyright 1995-2017 Shark Development Team</span></div>
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span><span class="comment"> * </span></div>
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span><span class="comment"> * &lt;BR&gt;&lt;HR&gt;</span></div>
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span><span class="comment"> * This file is part of Shark.</span></div>
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span><span class="comment"> * &lt;https://shark-ml.github.io/Shark/&gt;</span></div>
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span><span class="comment"> * </span></div>
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span><span class="comment"> * Shark is free software: you can redistribute it and/or modify</span></div>
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span><span class="comment"> * it under the terms of the GNU Lesser General Public License as published </span></div>
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span><span class="comment"> * by the Free Software Foundation, either version 3 of the License, or</span></div>
<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span><span class="comment"> * (at your option) any later version.</span></div>
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span><span class="comment"> * </span></div>
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span><span class="comment"> * Shark is distributed in the hope that it will be useful,</span></div>
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span><span class="comment"> * but WITHOUT ANY WARRANTY; without even the implied warranty of</span></div>
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span><span class="comment"> * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the</span></div>
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span><span class="comment"> * GNU Lesser General Public License for more details.</span></div>
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span><span class="comment"> * </span></div>
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span><span class="comment"> * You should have received a copy of the GNU Lesser General Public License</span></div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span><span class="comment"> * along with Shark.  If not, see &lt;http://www.gnu.org/licenses/&gt;.</span></div>
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span><span class="comment"> *</span></div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span><span class="comment"> */</span></div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span><span class="comment">//===========================================================================</span></div>
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span> </div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span> </div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span><span class="preprocessor">#ifndef SHARK_ALGORITHMS_TRAINERS_RFTRAINER_H</span></div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span><span class="preprocessor">#define SHARK_ALGORITHMS_TRAINERS_RFTRAINER_H</span></div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span> </div>
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span><span class="preprocessor">#include &lt;<a class="code" href="_abstract_weighted_trainer_8h.html">shark/Algorithms/Trainers/AbstractWeightedTrainer.h</a>&gt;</span></div>
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span><span class="preprocessor">#include &lt;<a class="code" href="_r_f_classifier_8h.html">shark/Models/Trees/RFClassifier.h</a>&gt;</span></div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span><span class="preprocessor">#include &lt;shark/Algorithms/Trainers/Impl/CART.h&gt;</span></div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span> </div>
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span><span class="preprocessor">#include &lt;vector&gt;</span></div>
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span><span class="preprocessor">#include &lt;limits&gt;</span></div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span> </div>
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span><span class="keyword">namespace </span><a class="code hl_namespace" href="namespaceshark.html" title="AbstractMultiObjectiveOptimizer.">shark</a> {</div>
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span><span class="comment"></span> </div>
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span><span class="comment">/// \brief Random Forest</span></div>
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span><span class="comment">///</span></div>
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span><span class="comment">/// Random Forest is an ensemble learner, that builds multiple binary decision trees.</span></div>
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span><span class="comment">/// The trees are built using a variant of the CART methodology</span></div>
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span><span class="comment">///</span></div>
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span><span class="comment">/// Typically 100+ trees are built, and classification/regression is done by combining</span></div>
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span><span class="comment">/// the results generated by each tree. Typically the a majority vote is used in the</span></div>
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span><span class="comment">/// classification case, and the mean is used in the regression case</span></div>
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span><span class="comment">///</span></div>
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span><span class="comment">/// Each tree is built based on a random subset of the total dataset. Furthermore</span></div>
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span><span class="comment">/// at each split, only a random subset of the attributes are investigated for</span></div>
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span><span class="comment">/// the best split</span></div>
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span><span class="comment">///</span></div>
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span><span class="comment">/// The node impurity is measured by the Gini criteria in the classification</span></div>
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span><span class="comment">/// case, and the total sum of squared errors in the regression case</span></div>
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span><span class="comment">///</span></div>
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span><span class="comment">/// After growing a maximum sized tree, the tree is added to the ensemble</span></div>
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span><span class="comment">/// without pruning.</span></div>
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span><span class="comment">///</span></div>
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span><span class="comment">/// For detailed information about Random Forest, see Random Forest</span></div>
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno">   68</span><span class="comment">/// by L. Breiman et al. 2001.</span></div>
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span><span class="comment">/// \ingroup supervised_trainer</span></div>
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span><span class="comment"></span> </div>
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span> </div>
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno">   72</span><span class="keyword">template</span>&lt;<span class="keyword">class</span> LabelType&gt;</div>
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno"><a class="line" href="classshark_1_1_r_f_trainer.html">   73</a></span><span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_r_f_trainer.html" title="Random Forest.">RFTrainer</a>;</div>
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno">   74</span> </div>
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno">   75</span><span class="keyword">template</span>&lt;&gt;</div>
<div class="foldopen" id="foldopen00076" data-start="{" data-end="};">
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno"><a class="line" href="classshark_1_1_r_f_trainer_3_01unsigned_01int_01_4.html">   76</a></span><span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_r_f_trainer.html" title="Random Forest.">RFTrainer</a>&lt;unsigned int&gt;</div>
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno">   77</span>: <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_abstract_weighted_trainer.html" title="Superclass of weighted supervised learning algorithms.">AbstractWeightedTrainer</a>&lt;RFClassifier&lt;unsigned int&gt; &gt;, <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_i_parameterizable.html" title="Top level interface for everything that holds parameters.">IParameterizable</a>&lt;RealVector&gt;</div>
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span>{</div>
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno">   79</span><span class="keyword">public</span>:<span class="comment"></span></div>
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span><span class="comment">    /// Construct and compute feature importances when training or not</span></div>
<div class="foldopen" id="foldopen00081" data-start="{" data-end="}">
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno"><a class="line" href="classshark_1_1_r_f_trainer_3_01unsigned_01int_01_4.html#a53204a1b23c9d4faa542ce5179162b9f">   81</a></span><span class="comment"></span>    <a class="code hl_function" href="classshark_1_1_r_f_trainer_3_01unsigned_01int_01_4.html#a53204a1b23c9d4faa542ce5179162b9f" title="Construct and compute feature importances when training or not.">RFTrainer</a>(<span class="keywordtype">bool</span> computeFeatureImportances = <span class="keyword">false</span>, <span class="keywordtype">bool</span> computeOOBerror = <span class="keyword">false</span>){</div>
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span>        m_computeFeatureImportances = computeFeatureImportances;</div>
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span>        m_computeOOBerror = computeOOBerror;</div>
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span>        m_numTrees = 100; </div>
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span>        m_min_samples_leaf = 1; </div>
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno">   86</span>        m_min_split = 2 * m_min_samples_leaf; </div>
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span>        m_max_depth = 10000;</div>
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno">   88</span>        m_min_impurity_split = 1e-10; </div>
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span>        m_epsilon = 1e-10;</div>
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span>        m_max_features = 0;</div>
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span>    }</div>
</div>
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span><span class="comment"></span> </div>
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span><span class="comment">    /// \brief From INameable: return the class name.</span></div>
<div class="foldopen" id="foldopen00094" data-start="{" data-end="}">
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno"><a class="line" href="classshark_1_1_r_f_trainer_3_01unsigned_01int_01_4.html#a12989f50963a1854f1fbc45c1009a839">   94</a></span><span class="comment"></span>    std::string <a class="code hl_function" href="classshark_1_1_r_f_trainer_3_01unsigned_01int_01_4.html#a12989f50963a1854f1fbc45c1009a839" title="From INameable: return the class name.">name</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> <span class="stringliteral">&quot;RFTrainer&quot;</span>; }</div>
</div>
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span><span class="comment"></span> </div>
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno">   97</span><span class="comment">    /// Set the number of random attributes to investigate at each node.</span></div>
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno">   98</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno">   99</span><span class="comment">    /// Defualt is 0 which is translated to sqrt(inputDim(data)) during training</span></div>
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno"><a class="line" href="classshark_1_1_r_f_trainer_3_01unsigned_01int_01_4.html#ad3c4d4e9765940e801e123de6fed5aea">  100</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_r_f_trainer_3_01unsigned_01int_01_4.html#ad3c4d4e9765940e801e123de6fed5aea">setMTry</a>(std::size_t mtry) { m_max_features = mtry; }</div>
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span><span class="comment"></span> </div>
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span><span class="comment">    /// Set the number of trees to grow. (default 100)</span></div>
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno"><a class="line" href="classshark_1_1_r_f_trainer_3_01unsigned_01int_01_4.html#ab5eee35787957f2a60b558ab92ed4797">  103</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_r_f_trainer_3_01unsigned_01int_01_4.html#ab5eee35787957f2a60b558ab92ed4797" title="Set the number of trees to grow. (default 100)">setNTrees</a>(std::size_t numTrees) {m_numTrees = numTrees;}</div>
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span>    <span class="comment"></span></div>
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno">  105</span><span class="comment">    /// Set Minimum number of samples that is split (default 2)</span></div>
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno"><a class="line" href="classshark_1_1_r_f_trainer_3_01unsigned_01int_01_4.html#a1fa08ae23d6263bc895a6a52625fe7f9">  106</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_r_f_trainer_3_01unsigned_01int_01_4.html#a1fa08ae23d6263bc895a6a52625fe7f9" title="Set Minimum number of samples that is split (default 2)">setMinSplit</a>(std::size_t numSamples) {m_min_split = numSamples;}</div>
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span><span class="comment"></span> </div>
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span><span class="comment">    /// Set Maximum depth of the tree (default 10000)</span></div>
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno"><a class="line" href="classshark_1_1_r_f_trainer_3_01unsigned_01int_01_4.html#a8ac7b0ff4a67b29d8d098d9a737c34c1">  109</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_r_f_trainer_3_01unsigned_01int_01_4.html#a8ac7b0ff4a67b29d8d098d9a737c34c1" title="Set Maximum depth of the tree (default 10000)">setMaxDepth</a>(std::size_t maxDepth) {m_max_depth = maxDepth;}</div>
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span>    <span class="comment"></span></div>
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span><span class="comment">    /// Controls when a node is considered pure. If set to 1, a node is pure</span></div>
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno">  112</span><span class="comment">    /// when it only consists of a single node.(default 1)</span></div>
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno"><a class="line" href="classshark_1_1_r_f_trainer_3_01unsigned_01int_01_4.html#af0462340f6a69e42aa4f790948762457">  113</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_r_f_trainer_3_01unsigned_01int_01_4.html#af0462340f6a69e42aa4f790948762457">setNodeSize</a>(std::size_t nodeSize) { m_min_samples_leaf = nodeSize; }</div>
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span><span class="comment"></span> </div>
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno">  115</span><span class="comment">    /// The minimum impurity below which a a node is considere pure (default 1.e-10)</span></div>
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno"><a class="line" href="classshark_1_1_r_f_trainer_3_01unsigned_01int_01_4.html#a650e555b24698db9bdc5d01fc764ddb7">  116</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_r_f_trainer_3_01unsigned_01int_01_4.html#a650e555b24698db9bdc5d01fc764ddb7" title="The minimum impurity below which a a node is considere pure (default 1.e-10)">minImpurity</a>(<span class="keywordtype">double</span> impurity) {m_min_impurity_split = impurity;}</div>
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span>    <span class="comment"></span></div>
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span><span class="comment">    /// The minimum dtsnace of features to be considered different (detault 1.e-10)</span></div>
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno"><a class="line" href="classshark_1_1_r_f_trainer_3_01unsigned_01int_01_4.html#a256c970e32f9064ce73cef2a658d0b87">  119</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_r_f_trainer_3_01unsigned_01int_01_4.html#a256c970e32f9064ce73cef2a658d0b87" title="The minimum dtsnace of features to be considered different (detault 1.e-10)">epsilon</a>(<span class="keywordtype">double</span> distance) {m_epsilon = distance;}</div>
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno">  120</span>    <span class="comment"></span></div>
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span><span class="comment">    /// Return the parameter vector.</span></div>
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno"><a class="line" href="classshark_1_1_r_f_trainer_3_01unsigned_01int_01_4.html#adefc3f79dc834760239e68f7a3ad4f24">  122</a></span><span class="comment"></span>    RealVector <a class="code hl_function" href="classshark_1_1_r_f_trainer_3_01unsigned_01int_01_4.html#adefc3f79dc834760239e68f7a3ad4f24" title="Return the parameter vector.">parameterVector</a>()<span class="keyword"> const</span>{<span class="keywordflow">return</span> RealVector();}</div>
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno">  123</span><span class="comment"></span> </div>
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span><span class="comment">    /// Set the parameter vector.</span></div>
<div class="foldopen" id="foldopen00125" data-start="{" data-end="}">
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno"><a class="line" href="classshark_1_1_r_f_trainer_3_01unsigned_01int_01_4.html#afc8c6b93118f575b2759d72b3dd39d85">  125</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_r_f_trainer_3_01unsigned_01int_01_4.html#afc8c6b93118f575b2759d72b3dd39d85" title="Set the parameter vector.">setParameterVector</a>(RealVector <span class="keyword">const</span>&amp; newParameters){</div>
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span>        <a class="code hl_define" href="_exception_8h.html#a73abb5049a0168d72a48e72dda41708b">SHARK_ASSERT</a>(newParameters.size() == 0);</div>
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno">  127</span>    }</div>
</div>
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span>    </div>
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span>    <span class="comment"></span></div>
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno">  130</span><span class="comment">    /// Train a random forest for classification.</span></div>
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno">  131</span><span class="comment"></span>    <span class="keyword">using </span><a class="code hl_class" href="classshark_1_1_abstract_weighted_trainer.html" title="Superclass of weighted supervised learning algorithms.">AbstractWeightedTrainer</a>&lt;<a class="code hl_class" href="classshark_1_1_r_f_classifier.html" title="Random Forest Classifier.">RFClassifier&lt;unsigned int&gt;</a> &gt;::train;</div>
<div class="foldopen" id="foldopen00132" data-start="{" data-end="}">
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno"><a class="line" href="classshark_1_1_r_f_trainer_3_01unsigned_01int_01_4.html#a4c1081a998508d12064ec653130f1a8f">  132</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_r_f_trainer_3_01unsigned_01int_01_4.html#a4c1081a998508d12064ec653130f1a8f">train</a>(<a class="code hl_class" href="classshark_1_1_r_f_classifier.html" title="Random Forest Classifier.">RFClassifier&lt;LabelType&gt;</a>&amp; model, <a class="code hl_class" href="classshark_1_1_weighted_labeled_data.html" title="Weighted data set for supervised learning.">WeightedLabeledData&lt;RealVector,LabelType&gt;</a> <span class="keyword">const</span>&amp; dataset){</div>
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span>        model.<a class="code hl_function" href="classshark_1_1_ensemble.html#ab04ccf3f9acb405f3fdedd125ff5ed1c" title="Removes all models from the ensemble.">clearModels</a>();</div>
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno">  134</span>        </div>
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span>        <span class="comment">//setup treebuilder</span></div>
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno">  136</span>        CART::TreeBuilder&lt;unsigned int,CART::ClassificationCriterion&gt; builder;</div>
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span>        builder.m_min_samples_leaf = m_min_samples_leaf;</div>
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno">  138</span>        builder.m_min_split = m_min_split;</div>
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno">  139</span>        builder.m_max_depth = m_max_depth;</div>
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno">  140</span>        builder.m_min_impurity_split = m_min_impurity_split;</div>
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno">  141</span>        builder.m_epsilon = m_epsilon;</div>
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno">  142</span>        builder.m_max_features = m_max_features? m_max_features: std::sqrt(<a class="code hl_function" href="group__shark__globals.html#gae537f0e90beb970397cd7bb9250984e2" title="Return the input dimensionality of a labeled dataset.">inputDimension</a>(dataset));</div>
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno">  143</span>        </div>
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno">  144</span>        <span class="comment">//copy data into single batch for easier lookup</span></div>
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno">  145</span>        blas::matrix&lt;double, blas::column_major&gt; data_train = createBatch&lt;RealVector&gt;(dataset.<a class="code hl_function" href="classshark_1_1_weighted_labeled_data.html#ad11b0613785e1c6f36f6dd5d32662ead" title="Access to the inputs as a separate container.">inputs</a>().<a class="code hl_function" href="group__shark__globals.html#gad9b0233e3adc882ed94f418f80767b09" title="Returns the range of elements.">elements</a>().begin(),dataset.<a class="code hl_function" href="classshark_1_1_weighted_labeled_data.html#ad11b0613785e1c6f36f6dd5d32662ead" title="Access to the inputs as a separate container.">inputs</a>().<a class="code hl_function" href="group__shark__globals.html#gad9b0233e3adc882ed94f418f80767b09" title="Returns the range of elements.">elements</a>().end());</div>
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno">  146</span>        <span class="keyword">auto</span> labels_train = createBatch&lt;LabelType&gt;(dataset.<a class="code hl_function" href="classshark_1_1_weighted_labeled_data.html#ae3ca78f96dd1c1881b04d3726213a136" title="Access to the labels as a separate container.">labels</a>().<a class="code hl_function" href="group__shark__globals.html#gad9b0233e3adc882ed94f418f80767b09" title="Returns the range of elements.">elements</a>().begin(),dataset.<a class="code hl_function" href="classshark_1_1_weighted_labeled_data.html#ae3ca78f96dd1c1881b04d3726213a136" title="Access to the labels as a separate container.">labels</a>().<a class="code hl_function" href="group__shark__globals.html#gad9b0233e3adc882ed94f418f80767b09" title="Returns the range of elements.">elements</a>().end());</div>
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno">  147</span>        <span class="keyword">auto</span> weights_train = createBatch&lt;double&gt;(dataset.weights().elements().begin(),dataset.weights().elements().end());</div>
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno">  148</span> </div>
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno">  149</span>        <span class="comment">//Setup seeds for the rng in the different threads</span></div>
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno">  150</span>        std::vector&lt;unsigned int&gt; seeds(m_numTrees);</div>
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno">  151</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span>&amp; seed: seeds) {</div>
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno">  152</span>            seed = <a class="code hl_function" href="namespaceshark_1_1random.html#aa64d4174eaf7111b03e0504eaa56b666" title="Draws a discrete number in {low,low+1,...,high} by drawing random numbers from rng.">random::discrete</a>(<a class="code hl_variable" href="namespaceshark_1_1random.html#ab5c1547eee483974d008d43f621a2234">random::globalRng</a>, 0u,std::numeric_limits&lt;unsigned int&gt;::max());</div>
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno">  153</span>        }</div>
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno">  154</span>        </div>
<div class="line"><a id="l00155" name="l00155"></a><span class="lineno">  155</span>        std::vector&lt;std::vector&lt;std::size_t&gt; &gt; complements;</div>
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno">  156</span> </div>
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno">  157</span>        <span class="comment">//Generate trees</span></div>
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno">  158</span>        <a class="code hl_define" href="_open_m_p_8h.html#a8a63d79e2c3625260e6092d933f21a98" title="Set of macros to help usage of OpenMP with Shark.">SHARK_PARALLEL_FOR</a>(<span class="keywordtype">int</span> t = 0; t &lt; m_numTrees; ++t){</div>
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno">  159</span>            random::rng_type rng(seeds[t]);</div>
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno">  160</span>            </div>
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno">  161</span>            <span class="comment">//Setup data for this tree</span></div>
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno">  162</span>            CART::Bootstrap&lt;blas::matrix&lt;double, blas::column_major&gt;, UIntVector&gt; <a class="code hl_function" href="namespaceshark.html#a23d76a81bf28d05f9357d236a63a17c8" title="Creates a bootstrap partition of a labeled dataset and returns it using weighting.">bootstrap</a>(rng, data_train,labels_train, weights_train);</div>
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno">  163</span>            <span class="keyword">auto</span> <span class="keyword">const</span>&amp; tree = builder.buildTree(rng, <a class="code hl_function" href="namespaceshark.html#a23d76a81bf28d05f9357d236a63a17c8" title="Creates a bootstrap partition of a labeled dataset and returns it using weighting.">bootstrap</a>);</div>
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno">  164</span>            </div>
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno">  165</span>            <a class="code hl_define" href="_open_m_p_8h.html#a6de33df9d72bea69f903cffb391e7121">SHARK_CRITICAL_REGION</a>{</div>
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno">  166</span>                model.<a class="code hl_function" href="classshark_1_1_ensemble.html#aaa96d4139d33b84477f8a41d9d12c8bb" title="Adds a new model to the ensemble.">addModel</a>(tree);</div>
<div class="line"><a id="l00167" name="l00167"></a><span class="lineno">  167</span>                complements.push_back(std::move(<a class="code hl_function" href="namespaceshark.html#a23d76a81bf28d05f9357d236a63a17c8" title="Creates a bootstrap partition of a labeled dataset and returns it using weighting.">bootstrap</a>.complement));</div>
<div class="line"><a id="l00168" name="l00168"></a><span class="lineno">  168</span>            }</div>
<div class="line"><a id="l00169" name="l00169"></a><span class="lineno">  169</span>        }</div>
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno">  170</span>        </div>
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno">  171</span>        <span class="keywordflow">if</span>(m_computeOOBerror)</div>
<div class="line"><a id="l00172" name="l00172"></a><span class="lineno">  172</span>            model.<a class="code hl_function" href="classshark_1_1_r_f_classifier.html#a336a6c2b13f973dd780644319dab4e8a" title="Compute oob error, given an oob dataset.">computeOOBerror</a>(complements, dataset.data());</div>
<div class="line"><a id="l00173" name="l00173"></a><span class="lineno">  173</span>        </div>
<div class="line"><a id="l00174" name="l00174"></a><span class="lineno">  174</span>        <span class="keywordflow">if</span>(m_computeFeatureImportances)</div>
<div class="line"><a id="l00175" name="l00175"></a><span class="lineno">  175</span>            model.<a class="code hl_function" href="classshark_1_1_r_f_classifier.html#a290cb9386878b0264faf15dae5dc2068">computeFeatureImportances</a>(complements,dataset.data(), <a class="code hl_variable" href="namespaceshark_1_1random.html#ab5c1547eee483974d008d43f621a2234">random::globalRng</a>);</div>
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno">  176</span>    }</div>
</div>
<div class="line"><a id="l00177" name="l00177"></a><span class="lineno">  177</span>    </div>
<div class="line"><a id="l00178" name="l00178"></a><span class="lineno">  178</span>    </div>
<div class="line"><a id="l00179" name="l00179"></a><span class="lineno">  179</span><span class="keyword">private</span>:</div>
<div class="line"><a id="l00180" name="l00180"></a><span class="lineno">  180</span>    <span class="keywordtype">bool</span> m_computeFeatureImportances;<span class="comment">///&lt; set true if the feature importances should be computed</span></div>
<div class="line"><a id="l00181" name="l00181"></a><span class="lineno">  181</span>    <span class="keywordtype">bool</span> m_computeOOBerror;<span class="comment">///&lt; set true if OOB error should be computed</span></div>
<div class="line"><a id="l00182" name="l00182"></a><span class="lineno">  182</span> </div>
<div class="line"><a id="l00183" name="l00183"></a><span class="lineno">  183</span>    <span class="keywordtype">long</span> m_numTrees; <span class="comment">///&lt; number of trees in the forest</span></div>
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno">  184</span>    std::size_t m_max_features;<span class="comment">///&lt; number of attributes to randomly test at each inner node</span></div>
<div class="line"><a id="l00185" name="l00185"></a><span class="lineno">  185</span>    std::size_t m_min_samples_leaf; <span class="comment">///&lt; minimum number of samples in a leaf node</span></div>
<div class="line"><a id="l00186" name="l00186"></a><span class="lineno">  186</span>    std::size_t m_min_split; <span class="comment">///&lt; minimum number of samples to be considered a split</span></div>
<div class="line"><a id="l00187" name="l00187"></a><span class="lineno">  187</span>    std::size_t m_max_depth;<span class="comment">///&lt; maximum depth of the tree</span></div>
<div class="line"><a id="l00188" name="l00188"></a><span class="lineno">  188</span>    <span class="keywordtype">double</span> m_epsilon;<span class="comment">///&lt; Minimum difference between two values to be considered different</span></div>
<div class="line"><a id="l00189" name="l00189"></a><span class="lineno">  189</span>    <span class="keywordtype">double</span> m_min_impurity_split;<span class="comment">///&lt; stops splitting when the impority is below a threshold</span></div>
<div class="line"><a id="l00190" name="l00190"></a><span class="lineno">  190</span>};</div>
</div>
<div class="line"><a id="l00191" name="l00191"></a><span class="lineno">  191</span> </div>
<div class="line"><a id="l00192" name="l00192"></a><span class="lineno">  192</span> </div>
<div class="line"><a id="l00193" name="l00193"></a><span class="lineno">  193</span><span class="keyword">template</span>&lt;&gt;</div>
<div class="foldopen" id="foldopen00194" data-start="{" data-end="};">
<div class="line"><a id="l00194" name="l00194"></a><span class="lineno"><a class="line" href="classshark_1_1_r_f_trainer_3_01_real_vector_01_4.html">  194</a></span><span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_r_f_trainer.html" title="Random Forest.">RFTrainer</a>&lt;RealVector&gt;</div>
<div class="line"><a id="l00195" name="l00195"></a><span class="lineno">  195</span>: <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_abstract_weighted_trainer.html" title="Superclass of weighted supervised learning algorithms.">AbstractWeightedTrainer</a>&lt;RFClassifier&lt;RealVector&gt; &gt;, <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_i_parameterizable.html" title="Top level interface for everything that holds parameters.">IParameterizable</a>&lt;RealVector&gt;</div>
<div class="line"><a id="l00196" name="l00196"></a><span class="lineno">  196</span>{</div>
<div class="line"><a id="l00197" name="l00197"></a><span class="lineno">  197</span><span class="keyword">public</span>:<span class="comment"></span></div>
<div class="line"><a id="l00198" name="l00198"></a><span class="lineno">  198</span><span class="comment">    /// Construct and compute feature importances when training or not</span></div>
<div class="foldopen" id="foldopen00199" data-start="{" data-end="}">
<div class="line"><a id="l00199" name="l00199"></a><span class="lineno"><a class="line" href="classshark_1_1_r_f_trainer_3_01_real_vector_01_4.html#aa70d6bf3520d6a5961cdacfa7f540d36">  199</a></span><span class="comment"></span>    <a class="code hl_function" href="classshark_1_1_r_f_trainer_3_01_real_vector_01_4.html#aa70d6bf3520d6a5961cdacfa7f540d36" title="Construct and compute feature importances when training or not.">RFTrainer</a>(<span class="keywordtype">bool</span> computeFeatureImportances = <span class="keyword">false</span>, <span class="keywordtype">bool</span> computeOOBerror = <span class="keyword">false</span>){</div>
<div class="line"><a id="l00200" name="l00200"></a><span class="lineno">  200</span>        m_computeFeatureImportances = computeFeatureImportances;</div>
<div class="line"><a id="l00201" name="l00201"></a><span class="lineno">  201</span>        m_computeOOBerror = computeOOBerror;</div>
<div class="line"><a id="l00202" name="l00202"></a><span class="lineno">  202</span>        m_numTrees = 100; </div>
<div class="line"><a id="l00203" name="l00203"></a><span class="lineno">  203</span>        m_min_samples_leaf = 1; </div>
<div class="line"><a id="l00204" name="l00204"></a><span class="lineno">  204</span>        m_min_split = 2 * m_min_samples_leaf; </div>
<div class="line"><a id="l00205" name="l00205"></a><span class="lineno">  205</span>        m_max_depth = 10000;</div>
<div class="line"><a id="l00206" name="l00206"></a><span class="lineno">  206</span>        m_min_impurity_split = 1e-10; </div>
<div class="line"><a id="l00207" name="l00207"></a><span class="lineno">  207</span>        m_epsilon = 1e-10;</div>
<div class="line"><a id="l00208" name="l00208"></a><span class="lineno">  208</span>        m_max_features = 0;</div>
<div class="line"><a id="l00209" name="l00209"></a><span class="lineno">  209</span>    }</div>
</div>
<div class="line"><a id="l00210" name="l00210"></a><span class="lineno">  210</span><span class="comment"></span> </div>
<div class="line"><a id="l00211" name="l00211"></a><span class="lineno">  211</span><span class="comment">    /// \brief From INameable: return the class name.</span></div>
<div class="foldopen" id="foldopen00212" data-start="{" data-end="}">
<div class="line"><a id="l00212" name="l00212"></a><span class="lineno"><a class="line" href="classshark_1_1_r_f_trainer_3_01_real_vector_01_4.html#ae1ad33346b1541c85c3d239306acd92b">  212</a></span><span class="comment"></span>    std::string <a class="code hl_function" href="classshark_1_1_r_f_trainer_3_01_real_vector_01_4.html#ae1ad33346b1541c85c3d239306acd92b" title="From INameable: return the class name.">name</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00213" name="l00213"></a><span class="lineno">  213</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> <span class="stringliteral">&quot;RFTrainer&quot;</span>; }</div>
</div>
<div class="line"><a id="l00214" name="l00214"></a><span class="lineno">  214</span><span class="comment"></span> </div>
<div class="line"><a id="l00215" name="l00215"></a><span class="lineno">  215</span><span class="comment">    /// Set the number of random attributes to investigate at each node.</span></div>
<div class="line"><a id="l00216" name="l00216"></a><span class="lineno">  216</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00217" name="l00217"></a><span class="lineno">  217</span><span class="comment">    /// Defualt is 0 which is translated to inputDim(data)/3 during training</span></div>
<div class="line"><a id="l00218" name="l00218"></a><span class="lineno"><a class="line" href="classshark_1_1_r_f_trainer_3_01_real_vector_01_4.html#a26132d8880d145e1e09e0e5c5b92e53a">  218</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_r_f_trainer_3_01_real_vector_01_4.html#a26132d8880d145e1e09e0e5c5b92e53a">setMTry</a>(std::size_t mtry) { m_max_features = mtry; }</div>
<div class="line"><a id="l00219" name="l00219"></a><span class="lineno">  219</span><span class="comment"></span> </div>
<div class="line"><a id="l00220" name="l00220"></a><span class="lineno">  220</span><span class="comment">    /// Set the number of trees to grow. (default 100)</span></div>
<div class="line"><a id="l00221" name="l00221"></a><span class="lineno"><a class="line" href="classshark_1_1_r_f_trainer_3_01_real_vector_01_4.html#a47b5618fe873957a5cd17ff5568361a3">  221</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_r_f_trainer_3_01_real_vector_01_4.html#a47b5618fe873957a5cd17ff5568361a3" title="Set the number of trees to grow. (default 100)">setNTrees</a>(std::size_t numTrees) {m_numTrees = numTrees;}</div>
<div class="line"><a id="l00222" name="l00222"></a><span class="lineno">  222</span>    <span class="comment"></span></div>
<div class="line"><a id="l00223" name="l00223"></a><span class="lineno">  223</span><span class="comment">    /// Set Minimum number of samples that is split (default 10)</span></div>
<div class="line"><a id="l00224" name="l00224"></a><span class="lineno"><a class="line" href="classshark_1_1_r_f_trainer_3_01_real_vector_01_4.html#a7973029c913a68140dc5082f8c5b5678">  224</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_r_f_trainer_3_01_real_vector_01_4.html#a7973029c913a68140dc5082f8c5b5678" title="Set Minimum number of samples that is split (default 10)">setMinSplit</a>(std::size_t numSamples) {m_min_split = numSamples;}</div>
<div class="line"><a id="l00225" name="l00225"></a><span class="lineno">  225</span><span class="comment"></span> </div>
<div class="line"><a id="l00226" name="l00226"></a><span class="lineno">  226</span><span class="comment">    /// Set Maximum depth of the tree (default 10000)</span></div>
<div class="line"><a id="l00227" name="l00227"></a><span class="lineno"><a class="line" href="classshark_1_1_r_f_trainer_3_01_real_vector_01_4.html#a82e8773ba20dcf97ea659ffc51713ba9">  227</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_r_f_trainer_3_01_real_vector_01_4.html#a82e8773ba20dcf97ea659ffc51713ba9" title="Set Maximum depth of the tree (default 10000)">setMaxDepth</a>(std::size_t maxDepth) {m_max_depth = maxDepth;}</div>
<div class="line"><a id="l00228" name="l00228"></a><span class="lineno">  228</span>    <span class="comment"></span></div>
<div class="line"><a id="l00229" name="l00229"></a><span class="lineno">  229</span><span class="comment">    /// Controls when a node is considered pure. If set to 1, a node is pure</span></div>
<div class="line"><a id="l00230" name="l00230"></a><span class="lineno">  230</span><span class="comment">    /// when it only consists of a single node.(default 5)</span></div>
<div class="line"><a id="l00231" name="l00231"></a><span class="lineno"><a class="line" href="classshark_1_1_r_f_trainer_3_01_real_vector_01_4.html#a03743949c6b502d953db82e98280de3e">  231</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_r_f_trainer_3_01_real_vector_01_4.html#a03743949c6b502d953db82e98280de3e">setNodeSize</a>(std::size_t nodeSize) { m_min_samples_leaf = nodeSize; }</div>
<div class="line"><a id="l00232" name="l00232"></a><span class="lineno">  232</span><span class="comment"></span> </div>
<div class="line"><a id="l00233" name="l00233"></a><span class="lineno">  233</span><span class="comment">    /// The minimum impurity below which a a node is considere pure (default 1.e-10)</span></div>
<div class="line"><a id="l00234" name="l00234"></a><span class="lineno"><a class="line" href="classshark_1_1_r_f_trainer_3_01_real_vector_01_4.html#a0afd1b5c06d9eeea0f09ce75b1c3a160">  234</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_r_f_trainer_3_01_real_vector_01_4.html#a0afd1b5c06d9eeea0f09ce75b1c3a160" title="The minimum impurity below which a a node is considere pure (default 1.e-10)">minImpurity</a>(<span class="keywordtype">double</span> impurity) {m_min_impurity_split = impurity;}</div>
<div class="line"><a id="l00235" name="l00235"></a><span class="lineno">  235</span>    <span class="comment"></span></div>
<div class="line"><a id="l00236" name="l00236"></a><span class="lineno">  236</span><span class="comment">    /// The minimum dtsnace of features to be considered different (detault 1.e-10)</span></div>
<div class="line"><a id="l00237" name="l00237"></a><span class="lineno"><a class="line" href="classshark_1_1_r_f_trainer_3_01_real_vector_01_4.html#a15b07750636e93b6caf5ce721226a0c5">  237</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_r_f_trainer_3_01_real_vector_01_4.html#a15b07750636e93b6caf5ce721226a0c5" title="The minimum dtsnace of features to be considered different (detault 1.e-10)">epsilon</a>(<span class="keywordtype">double</span> distance) {m_epsilon = distance;}</div>
<div class="line"><a id="l00238" name="l00238"></a><span class="lineno">  238</span>    <span class="comment"></span></div>
<div class="line"><a id="l00239" name="l00239"></a><span class="lineno">  239</span><span class="comment">    /// Return the parameter vector.</span></div>
<div class="line"><a id="l00240" name="l00240"></a><span class="lineno"><a class="line" href="classshark_1_1_r_f_trainer_3_01_real_vector_01_4.html#a9a0674d771e229c820f0d3dfa24b38a2">  240</a></span><span class="comment"></span>    RealVector <a class="code hl_function" href="classshark_1_1_r_f_trainer_3_01_real_vector_01_4.html#a9a0674d771e229c820f0d3dfa24b38a2" title="Return the parameter vector.">parameterVector</a>()<span class="keyword"> const</span>{ <span class="keywordflow">return</span> RealVector();}</div>
<div class="line"><a id="l00241" name="l00241"></a><span class="lineno">  241</span><span class="comment"></span> </div>
<div class="line"><a id="l00242" name="l00242"></a><span class="lineno">  242</span><span class="comment">    /// Set the parameter vector.</span></div>
<div class="foldopen" id="foldopen00243" data-start="{" data-end="}">
<div class="line"><a id="l00243" name="l00243"></a><span class="lineno"><a class="line" href="classshark_1_1_r_f_trainer_3_01_real_vector_01_4.html#a2172de721b6e63265d67c56076036121">  243</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_r_f_trainer_3_01_real_vector_01_4.html#a2172de721b6e63265d67c56076036121" title="Set the parameter vector.">setParameterVector</a>(RealVector <span class="keyword">const</span>&amp; newParameters){</div>
<div class="line"><a id="l00244" name="l00244"></a><span class="lineno">  244</span>        <a class="code hl_define" href="_exception_8h.html#a73abb5049a0168d72a48e72dda41708b">SHARK_ASSERT</a>(newParameters.size() == 0);</div>
<div class="line"><a id="l00245" name="l00245"></a><span class="lineno">  245</span>    }</div>
</div>
<div class="line"><a id="l00246" name="l00246"></a><span class="lineno">  246</span>    </div>
<div class="line"><a id="l00247" name="l00247"></a><span class="lineno">  247</span>    <span class="comment"></span></div>
<div class="line"><a id="l00248" name="l00248"></a><span class="lineno">  248</span><span class="comment">    /// Train a random forest for classification.</span></div>
<div class="foldopen" id="foldopen00249" data-start="{" data-end="}">
<div class="line"><a id="l00249" name="l00249"></a><span class="lineno"><a class="line" href="classshark_1_1_r_f_trainer_3_01_real_vector_01_4.html#a4975033b328b481f5bbfa2fea88ddcd9">  249</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_r_f_trainer_3_01_real_vector_01_4.html#a4975033b328b481f5bbfa2fea88ddcd9" title="Train a random forest for classification.">train</a>(<a class="code hl_class" href="classshark_1_1_r_f_classifier.html" title="Random Forest Classifier.">RFClassifier&lt;LabelType&gt;</a>&amp; model, <a class="code hl_class" href="classshark_1_1_weighted_labeled_data.html" title="Weighted data set for supervised learning.">WeightedLabeledData&lt;RealVector,LabelType&gt;</a> <span class="keyword">const</span>&amp; dataset){</div>
<div class="line"><a id="l00250" name="l00250"></a><span class="lineno">  250</span>        model.<a class="code hl_function" href="classshark_1_1_ensemble.html#ab04ccf3f9acb405f3fdedd125ff5ed1c" title="Removes all models from the ensemble.">clearModels</a>();</div>
<div class="line"><a id="l00251" name="l00251"></a><span class="lineno">  251</span>        <span class="comment">//setup treebuilder</span></div>
<div class="line"><a id="l00252" name="l00252"></a><span class="lineno">  252</span>        CART::TreeBuilder&lt;RealVector,CART::MSECriterion&gt; builder;</div>
<div class="line"><a id="l00253" name="l00253"></a><span class="lineno">  253</span>        builder.m_min_samples_leaf = m_min_samples_leaf;</div>
<div class="line"><a id="l00254" name="l00254"></a><span class="lineno">  254</span>        builder.m_min_split = m_min_split;</div>
<div class="line"><a id="l00255" name="l00255"></a><span class="lineno">  255</span>        builder.m_max_depth = m_max_depth;</div>
<div class="line"><a id="l00256" name="l00256"></a><span class="lineno">  256</span>        builder.m_min_impurity_split = m_min_impurity_split;</div>
<div class="line"><a id="l00257" name="l00257"></a><span class="lineno">  257</span>        builder.m_epsilon = m_epsilon;</div>
<div class="line"><a id="l00258" name="l00258"></a><span class="lineno">  258</span>        builder.m_max_features = m_max_features? m_max_features: <a class="code hl_function" href="group__shark__globals.html#gae537f0e90beb970397cd7bb9250984e2" title="Return the input dimensionality of a labeled dataset.">inputDimension</a>(dataset)/3;</div>
<div class="line"><a id="l00259" name="l00259"></a><span class="lineno">  259</span>        <span class="comment">//copy data into single batch for easier lookup</span></div>
<div class="line"><a id="l00260" name="l00260"></a><span class="lineno">  260</span>        blas::matrix&lt;double, blas::column_major&gt; data_train = createBatch&lt;RealVector&gt;(dataset.<a class="code hl_function" href="classshark_1_1_weighted_labeled_data.html#ad11b0613785e1c6f36f6dd5d32662ead" title="Access to the inputs as a separate container.">inputs</a>().<a class="code hl_function" href="group__shark__globals.html#gad9b0233e3adc882ed94f418f80767b09" title="Returns the range of elements.">elements</a>().begin(),dataset.<a class="code hl_function" href="classshark_1_1_weighted_labeled_data.html#ad11b0613785e1c6f36f6dd5d32662ead" title="Access to the inputs as a separate container.">inputs</a>().<a class="code hl_function" href="group__shark__globals.html#gad9b0233e3adc882ed94f418f80767b09" title="Returns the range of elements.">elements</a>().end());</div>
<div class="line"><a id="l00261" name="l00261"></a><span class="lineno">  261</span>        <span class="keyword">auto</span> labels_train = createBatch&lt;LabelType&gt;(dataset.<a class="code hl_function" href="classshark_1_1_weighted_labeled_data.html#ae3ca78f96dd1c1881b04d3726213a136" title="Access to the labels as a separate container.">labels</a>().<a class="code hl_function" href="group__shark__globals.html#gad9b0233e3adc882ed94f418f80767b09" title="Returns the range of elements.">elements</a>().begin(),dataset.<a class="code hl_function" href="classshark_1_1_weighted_labeled_data.html#ae3ca78f96dd1c1881b04d3726213a136" title="Access to the labels as a separate container.">labels</a>().<a class="code hl_function" href="group__shark__globals.html#gad9b0233e3adc882ed94f418f80767b09" title="Returns the range of elements.">elements</a>().end());</div>
<div class="line"><a id="l00262" name="l00262"></a><span class="lineno">  262</span>        <span class="keyword">auto</span> weights_train = createBatch&lt;double&gt;(dataset.weights().elements().begin(),dataset.weights().elements().end());</div>
<div class="line"><a id="l00263" name="l00263"></a><span class="lineno">  263</span>        </div>
<div class="line"><a id="l00264" name="l00264"></a><span class="lineno">  264</span>        <span class="comment">//Setup seeds for the rng in the different threads</span></div>
<div class="line"><a id="l00265" name="l00265"></a><span class="lineno">  265</span>        std::vector&lt;unsigned int&gt; seeds(m_numTrees);</div>
<div class="line"><a id="l00266" name="l00266"></a><span class="lineno">  266</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span>&amp; seed: seeds) {</div>
<div class="line"><a id="l00267" name="l00267"></a><span class="lineno">  267</span>            seed = <a class="code hl_function" href="namespaceshark_1_1random.html#aa64d4174eaf7111b03e0504eaa56b666" title="Draws a discrete number in {low,low+1,...,high} by drawing random numbers from rng.">random::discrete</a>(<a class="code hl_variable" href="namespaceshark_1_1random.html#ab5c1547eee483974d008d43f621a2234">random::globalRng</a>, 0u,std::numeric_limits&lt;unsigned int&gt;::max());</div>
<div class="line"><a id="l00268" name="l00268"></a><span class="lineno">  268</span>        }</div>
<div class="line"><a id="l00269" name="l00269"></a><span class="lineno">  269</span>        </div>
<div class="line"><a id="l00270" name="l00270"></a><span class="lineno">  270</span>        std::vector&lt;std::vector&lt;std::size_t&gt; &gt; complements;</div>
<div class="line"><a id="l00271" name="l00271"></a><span class="lineno">  271</span> </div>
<div class="line"><a id="l00272" name="l00272"></a><span class="lineno">  272</span>        <span class="comment">//Generate trees</span></div>
<div class="line"><a id="l00273" name="l00273"></a><span class="lineno">  273</span>        <a class="code hl_define" href="_open_m_p_8h.html#a8a63d79e2c3625260e6092d933f21a98" title="Set of macros to help usage of OpenMP with Shark.">SHARK_PARALLEL_FOR</a>(<span class="keywordtype">int</span> t = 0; t &lt; m_numTrees; ++t){</div>
<div class="line"><a id="l00274" name="l00274"></a><span class="lineno">  274</span>            random::rng_type rng{seeds[t]};</div>
<div class="line"><a id="l00275" name="l00275"></a><span class="lineno">  275</span>            </div>
<div class="line"><a id="l00276" name="l00276"></a><span class="lineno">  276</span>            <span class="comment">//Setup data for this tree</span></div>
<div class="line"><a id="l00277" name="l00277"></a><span class="lineno">  277</span>            CART::Bootstrap&lt;blas::matrix&lt;double, blas::column_major&gt;, RealMatrix&gt; <a class="code hl_function" href="namespaceshark.html#a23d76a81bf28d05f9357d236a63a17c8" title="Creates a bootstrap partition of a labeled dataset and returns it using weighting.">bootstrap</a>(rng, data_train,labels_train, weights_train);</div>
<div class="line"><a id="l00278" name="l00278"></a><span class="lineno">  278</span>            <span class="keyword">auto</span> <span class="keyword">const</span>&amp; tree = builder.buildTree(rng, <a class="code hl_function" href="namespaceshark.html#a23d76a81bf28d05f9357d236a63a17c8" title="Creates a bootstrap partition of a labeled dataset and returns it using weighting.">bootstrap</a>);</div>
<div class="line"><a id="l00279" name="l00279"></a><span class="lineno">  279</span>            </div>
<div class="line"><a id="l00280" name="l00280"></a><span class="lineno">  280</span>            <a class="code hl_define" href="_open_m_p_8h.html#a6de33df9d72bea69f903cffb391e7121">SHARK_CRITICAL_REGION</a>{</div>
<div class="line"><a id="l00281" name="l00281"></a><span class="lineno">  281</span>                model.<a class="code hl_function" href="classshark_1_1_ensemble.html#aaa96d4139d33b84477f8a41d9d12c8bb" title="Adds a new model to the ensemble.">addModel</a>(tree);</div>
<div class="line"><a id="l00282" name="l00282"></a><span class="lineno">  282</span>                complements.push_back(std::move(<a class="code hl_function" href="namespaceshark.html#a23d76a81bf28d05f9357d236a63a17c8" title="Creates a bootstrap partition of a labeled dataset and returns it using weighting.">bootstrap</a>.complement));</div>
<div class="line"><a id="l00283" name="l00283"></a><span class="lineno">  283</span>            }</div>
<div class="line"><a id="l00284" name="l00284"></a><span class="lineno">  284</span>        }</div>
<div class="line"><a id="l00285" name="l00285"></a><span class="lineno">  285</span>        </div>
<div class="line"><a id="l00286" name="l00286"></a><span class="lineno">  286</span>        <span class="keywordflow">if</span>(m_computeOOBerror)</div>
<div class="line"><a id="l00287" name="l00287"></a><span class="lineno">  287</span>            model.<a class="code hl_function" href="classshark_1_1_r_f_classifier.html#a336a6c2b13f973dd780644319dab4e8a" title="Compute oob error, given an oob dataset.">computeOOBerror</a>(complements,dataset.data());</div>
<div class="line"><a id="l00288" name="l00288"></a><span class="lineno">  288</span>        </div>
<div class="line"><a id="l00289" name="l00289"></a><span class="lineno">  289</span>        <span class="keywordflow">if</span>(m_computeFeatureImportances)</div>
<div class="line"><a id="l00290" name="l00290"></a><span class="lineno">  290</span>            model.<a class="code hl_function" href="classshark_1_1_r_f_classifier.html#a290cb9386878b0264faf15dae5dc2068">computeFeatureImportances</a>(complements,dataset.data(), <a class="code hl_variable" href="namespaceshark_1_1random.html#ab5c1547eee483974d008d43f621a2234">random::globalRng</a>);</div>
<div class="line"><a id="l00291" name="l00291"></a><span class="lineno">  291</span>    }</div>
</div>
<div class="line"><a id="l00292" name="l00292"></a><span class="lineno">  292</span>    </div>
<div class="line"><a id="l00293" name="l00293"></a><span class="lineno">  293</span>    </div>
<div class="line"><a id="l00294" name="l00294"></a><span class="lineno">  294</span><span class="keyword">private</span>:</div>
<div class="line"><a id="l00295" name="l00295"></a><span class="lineno">  295</span>    <span class="keywordtype">bool</span> m_computeFeatureImportances;<span class="comment">///&lt; set true if the feature importances should be computed</span></div>
<div class="line"><a id="l00296" name="l00296"></a><span class="lineno">  296</span>    <span class="keywordtype">bool</span> m_computeOOBerror;<span class="comment">///&lt; set true if OOB error should be computed</span></div>
<div class="line"><a id="l00297" name="l00297"></a><span class="lineno">  297</span> </div>
<div class="line"><a id="l00298" name="l00298"></a><span class="lineno">  298</span>    <span class="keywordtype">long</span> m_numTrees; <span class="comment">///&lt; number of trees in the forest</span></div>
<div class="line"><a id="l00299" name="l00299"></a><span class="lineno">  299</span>    std::size_t m_max_features;<span class="comment">///&lt; number of attributes to randomly test at each inner node</span></div>
<div class="line"><a id="l00300" name="l00300"></a><span class="lineno">  300</span>    std::size_t m_min_samples_leaf; <span class="comment">///&lt; minimum number of samples in a leaf node</span></div>
<div class="line"><a id="l00301" name="l00301"></a><span class="lineno">  301</span>    std::size_t m_min_split; <span class="comment">///&lt; minimum number of samples to be considered a split</span></div>
<div class="line"><a id="l00302" name="l00302"></a><span class="lineno">  302</span>    std::size_t m_max_depth;<span class="comment">///&lt; maximum depth of the tree</span></div>
<div class="line"><a id="l00303" name="l00303"></a><span class="lineno">  303</span>    <span class="keywordtype">double</span> m_epsilon;<span class="comment">///&lt; Minimum difference between two values to be considered different</span></div>
<div class="line"><a id="l00304" name="l00304"></a><span class="lineno">  304</span>    <span class="keywordtype">double</span> m_min_impurity_split;<span class="comment">///&lt; stops splitting when the impority is below a threshold</span></div>
<div class="line"><a id="l00305" name="l00305"></a><span class="lineno">  305</span>};</div>
</div>
<div class="line"><a id="l00306" name="l00306"></a><span class="lineno">  306</span> </div>
<div class="line"><a id="l00307" name="l00307"></a><span class="lineno">  307</span> </div>
<div class="line"><a id="l00308" name="l00308"></a><span class="lineno">  308</span>}</div>
<div class="line"><a id="l00309" name="l00309"></a><span class="lineno">  309</span><span class="preprocessor">#endif</span></div>
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